This file is my general teaching statement, written at a fairly high level of abstraction; this is part of my job market file. The post directly below this one contains a more detailed description of my teaching portfolio.

Teaching statement

I am strongly committed to the craft of teaching, and I am particularly interested in teaching courses that students often dread: statistics and computing for social science and research methods more generally. My broadest aim when teaching is to leave students with a healthy, rigorous skepticism of big claims: most of my students do not go on to conduct scientific research or need to recall formulae on a daily basis, but as citizens and employees, they do frequently need to critically evaluate the arguments of politicians and coworkers. I believe that learning to reason like a good social scientist serves as an object lesson in critical thinking more generally — students may not recall the nuts and bolts years later, but the experience of successfully mastering those details once leaves an appreciation for the art of reasoning that is hard to replicate by other means.

At a more concrete level, my goal is for students to master difficult but important content that allows them an in-depth experience of what it is like to confidently evaluate contentious claims. I have three fundamental goals for my teaching in this respect.

First, I hope for students to come away with some mastery of the relevant theoretical concepts — I want them to understand, rather than believe, what scholars claim. I strongly believe in presenting students with material in their “zone of proximal development”: rather than inundate students with long lists of theoreticians or statisticians and expect them to struggle to memorize reductive renditions of “who said what”, I present the basic ideas in sociological research methods and expect students to understand the arguments. For example, in my statistics lectures, I present brief demonstrations of properties of the variance that are new to students but do not go beyond the level of algebra. While it is possible, and common, to simply tell students these properties, I generally do not ask students to memorize content on my say-so; if there is a complex or novel idea that I believe that they can understand, I ask them to understand it, and if an idea is truly too complex for a given course, I avoid wading in those waters. In short, I want students to have a clear narrative arc for a course where they feel ownership of the ideas.

Second, I strongly believe in student-oriented, hands-on learning. Even when I deliver lectures on statistics to more than $100$ students, I generally do “call and response”. I have been surprised at how well this works; if I put in the effort to learn students’ names when they volunteer information, I have found that I can even cold call students in large math classes and get enthusiastic answers, even when students are not confident in their answer. On the level of the material itself, I hope for students to take ownership of the material. I expect students to feel comfortable with some pen-and-paper calculations and to be confident using statistical software. Even in the age of large language models, I believe it is important for students to actually play with formulae, to be able to make mistakes and achieve the satisfaction of struggling and getting a correct answer, rather than to merely loosely understand the “concept”. I have found that many students in fact thrive when they see that, for example, they can calculate the correlation coefficient by hand for a small set of data and observe that it obeys all of the expected properties — the sign goes the direction they would hope and the strength is about what they would guess.

Finally, I seek to make the material relevant to students. When I teach statistics, the course culminates in a research project of their choosing. Students use real world data (I allow them to select between the GSS and CPS) and use Stata or Python to answer questions that they are actually interested in. I provide detailed feedback on project proposals and the final products, even in large classes (with the help of TAs, of course). I encourage students to explore controversial questions and to put their own views to the test. Students report to me sometimes that they update their priors based on the results, which I think is a strong measure of success: I know that I have given students the tools to answer questions with so much confidence that they are willing to change their minds.

Experience

I have served as the instructor of record for “Statistics for Sociologists I” (SOC360 at UW-Madison) on five occasions and the TA once. Four of those semesters were in-person, with three being during the regular semester and one during a small summer course. I have also lectured “Social Research Methods” (SOC357) twice and “Data Management for Social Science” (SOC365) once.

As a TA, I taught two sections of “Statistics for Sociologists II” (SOC361) for one semester, between three and five sections of “American Society: How It Really Works” (SOC125) for four semesters and taught five sections of “Population Problems” (SOC170) for two semesters.

In teaching these courses, I received excellent student reviews. I have also won or been nominated by my department for a number of teaching awards. In 2020, I was the winner of an Excellence in Teaching by a Teaching Assistant award, a University-wide award. I was also the UW Sociology Department’s Nominee for the 2023–2024 Letters and Science Teaching Mentors Program as well as the Department’s Nominee for the Campus Exceptional Service Award in 2021.

I put a great deal of effort into my teaching. I crafted a portion of my own introductory quantitative methods textbook available here and produced a lecture series about statistics and Stata. A fuller description of my teaching portfolio, including lecture slides and code, is available here on my website.